ARCANE: An Efficient Architecture for Exact Machine Unlearning
Open accessAbstract
Recently users’ right-to-be-forgotten is stipulated by many laws and regulations. However, only removing the data from the dataset is not enough, as machine learning models would memorize the training data once the data is involved in model training, increasing the risk of exposing users’ privacy. To solve this problem, currently, the straightforward method, naive retraining, is to discard these data and retrain the model from scratch, which is reliable but brings much computational and time overhead. In this paper, we propose an exact unlearning architecture called ARCANE. Based on ensemble learning, we transform the naive retraining into multiple one-class classification tasks to reduce retraining cost while ensuring model performance, especially in the case of a large number of unlearning requests not considered by previous works. Then we further introduce data preprocessing methods to reduce the retraining overhead and speed up the unlearning, which includes representative data selection for redundancy removal, training state saving to reuse previous calculation results, and sorting to cope with unlearning requests of different distributions. We extensively evaluate ARCANE on three typical datasets with three common model architectures. Experiment results show the effectiveness and superiority of ARCANE over both the naive retraining and the state-of-the-art method in terms of model performance and unlearning speed.
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The above unlearning schemes usually need to cache a large number of intermediate results to complete the unlearning process, which consumes a lot of storage space.
“Nevertheless, the above unlearning schemes [30, 33, 69] usually need to cache a large number of intermediate results to complete the unlearning process. This will consume a lot of storage space.”
From Machine Unlearning: A Survey (2024) · cites this paper as [69]
Section 3.3, “Training State Saving” (page unavailable), says ARCANE saves model parameters after each data block and incurs space overhead for those saved states. It does not establish that unlearning schemes usually cache a large number of intermediate results or that this consumes a lot of storage space. (LLM, conf=0.84)
Experimental — accuracy not yet measuredThe unlearning schemes proposed in References [29, 30, 33, 47, 68, 69] are essentially based on a retraining mechanism that naturally has a verifiability property.
“The unlearning schemes proposed in References [29, 30, 33, 47, 68, 69] are essentially based on a retraining mechanism that naturally has a verifiability property.”
From Machine Unlearning: A Survey (2024) · cites this paper as [69]
Section 1.2 describes exact unlearning as removing data and retraining, and calls that approach naturally reliable. The Discussion also says exact unlearning is based on retraining at the algorithmic level. Neither passage establishes the distinct claim that these schemes naturally have a verifiability property. (LLM, conf=0.83)
Experimental — accuracy not yet measured
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- Authors
- Yan, H., Li, X., Guo, Z., Li, H., Li, F., & Lin, X.
- Affiliations
- Xidian University (CN)
- University of Guelph (CA)
- Corresponding author
- Yan, Haonan
- Venue
- Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence
- Published
- 2022-07
- Pages
- 4006-4013
- OpenAlex
- W4221158899
- Cited by
- 97
- References
- 23
- Citations by year
- 2026: 18 · 2025: 43 · 2024: 24 · 2023: 12
- FWCI
- 10.92
- Type
- conference paper
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- International Joint Conference on Artificial Intelligence (publishedVersion)
- Funders
- University of Guelph
- National Natural Science Foundation of China (2018TD-007, 61932015, B16037)
- Higher Education Discipline Innovation Project (2018TD-007, 61932015, B16037)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Social Sciences › Sociology and Political Science › Privacy, Security, and Data Protection
- Physical Sciences › Computer Science › Artificial Intelligence › Adversarial Robustness in Machine Learning
- Keywords
- machine unlearning, right to be forgotten, ensemble learning, one-class classification, data privacy, Arcane
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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